Compressive Network Analysis

Modern data acquisition routinely produces massive amounts of network data. Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected with the classical theory of statistical learning and signal processing. In this paper, we present...

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Veröffentlicht in:IEEE transactions on automatic control Jg. 59; H. 11; S. 2946 - 2961
Hauptverfasser: Xiaoye Jiang, Yuan Yao, Han Liu, Guibas, Leonidas
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.11.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algebra
Algorithms
Atmospheric modeling
Automatic control
Communities
Compressed sensing
Computer networks
Data models
Dictionaries
Disengaging
Joints
Network analysis
Networks
Noise
Vectors
Clique detection
network data analysis
restricted isometry property
Radon basis pursuit
compressive sensing
ISSN:0018-9286, 1558-2523
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Zusammenfassung:Modern data acquisition routinely produces massive amounts of network data. Though many methods and models have been proposed to analyze such data, the research of network data is largely disconnected with the classical theory of statistical learning and signal processing. In this paper, we present a new framework for modeling network data, which connects two seemingly different areas: network data analysis and compressed sensing. From a nonparametric perspective, we model an observed network using a large dictionary. In particular, we consider the network clique detection problem and show connections between our formulation with a new algebraic tool, namely Randon basis pursuit in homogeneous spaces. Such a connection allows us to identify rigorous recovery conditions for clique detection problems. Though this paper is mainly conceptual, we also develop practical approximation algorithms for solving empirical problems and demonstrate their usefulness on real-world datasets.
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ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2014.2351712